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%global _empty_manifest_terminate_build 0
Name: python-greykite
Version: 0.5.0
Release: 1
Summary: A python package for flexible forecasting
License: BSD-2-CLAUSE
URL: https://github.com/linkedin/greykite
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4c/a9/2bcbcc31b4db440490d0be818fb0cc4b598a116bff1339f76b4eeed03264/greykite-0.5.0.tar.gz
BuildArch: noarch
Requires: python3-cvxpy
Requires: python3-dill
Requires: python3-holidays-ext
Requires: python3-ipython
Requires: python3-matplotlib
Requires: python3-numpy
Requires: python3-osqp
Requires: python3-overrides
Requires: python3-pandas
Requires: python3-patsy
Requires: python3-plotly
Requires: python3-pmdarima
Requires: python3-pytest
Requires: python3-pytest-runner
Requires: python3-scipy
Requires: python3-six
Requires: python3-scikit-learn
Requires: python3-statsmodels
Requires: python3-testfixtures
Requires: python3-tqdm
%description
The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.
Silverkite algorithm works well on most time series, and is especially adept for those with changepoints in trend or seasonality,
event/holiday effects, and temporal dependencies.
Its forecasts are interpretable and therefore useful for trusted decision-making and insights.
The Greykite library provides a framework that makes it easy to develop a good forecast model,
with exploratory data analysis, outlier/anomaly preprocessing, feature extraction and engineering, grid search,
evaluation, benchmarking, and plotting.
Other open source algorithms can be supported through Greykite’s interface to take advantage of this framework,
as listed below.
For a demo, please see our `quickstart <https://linkedin.github.io/greykite/get_started>`_.
%package -n python3-greykite
Summary: A python package for flexible forecasting
Provides: python-greykite
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-greykite
The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.
Silverkite algorithm works well on most time series, and is especially adept for those with changepoints in trend or seasonality,
event/holiday effects, and temporal dependencies.
Its forecasts are interpretable and therefore useful for trusted decision-making and insights.
The Greykite library provides a framework that makes it easy to develop a good forecast model,
with exploratory data analysis, outlier/anomaly preprocessing, feature extraction and engineering, grid search,
evaluation, benchmarking, and plotting.
Other open source algorithms can be supported through Greykite’s interface to take advantage of this framework,
as listed below.
For a demo, please see our `quickstart <https://linkedin.github.io/greykite/get_started>`_.
%package help
Summary: Development documents and examples for greykite
Provides: python3-greykite-doc
%description help
The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.
Silverkite algorithm works well on most time series, and is especially adept for those with changepoints in trend or seasonality,
event/holiday effects, and temporal dependencies.
Its forecasts are interpretable and therefore useful for trusted decision-making and insights.
The Greykite library provides a framework that makes it easy to develop a good forecast model,
with exploratory data analysis, outlier/anomaly preprocessing, feature extraction and engineering, grid search,
evaluation, benchmarking, and plotting.
Other open source algorithms can be supported through Greykite’s interface to take advantage of this framework,
as listed below.
For a demo, please see our `quickstart <https://linkedin.github.io/greykite/get_started>`_.
%prep
%autosetup -n greykite-0.5.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-greykite -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 25 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.0-1
- Package Spec generated
|
